{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Basic Principles"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "Show different ways to present statistical data.\n",
    "\n",
    "This script is written in *MATLAB* or *IPpython* style, to show how\n",
    "best to use Python interactively.\n",
    "Note than in *IPython*, the ``show()`` commands are automatically generated.\n",
    "The examples contain:\n",
    "\n",
    "- scatter plots\n",
    "- histograms\n",
    "- KDE\n",
    "- errorbars\n",
    "- boxplots\n",
    "- probplots\n",
    "- cumulative density functions\n",
    "- regression fits\n",
    "\n",
    "Author: thomas haslwanter, April-2020"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Getting things ready"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "First, import the libraries that you are going to need. You could also do\n",
    "that later, but it is better style to do that at the beginning.\n",
    "pylab imports the numpy, scipy, and matplotlib.pyplot libraries into the\n",
    "current environment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import scipy.stats as stats\n",
    "import seaborn as sns\n",
    "\n",
    "sns.set_context('notebook')\n",
    "sns.set_style('darkgrid')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "# Generate data that are normally distributed\n",
    "x = np.random.randn(50)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Scatter plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x,'.')\n",
    "plt.title('Scatter Plot')\n",
    "plt.xlabel('X')\n",
    "plt.ylabel('Y')\n",
    "plt.draw()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Histogram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Histogram, default settings')"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(x)\n",
    "plt.xlabel('Data Values')\n",
    "plt.ylabel('Frequency')\n",
    "plt.title('Histogram, default settings')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = np.random.randn(1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Histogram, 25 bins')"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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MzEwEBwdbOhsREclEUjnExMRg48aNyMnJgUqlwoABAxAVFWXpbEREJBNJ5aBUKhEdHY3o6GhL56EmytXdBS5O4rejRuNWw9REZGmSymHfvn1YtGgRbt++DUEQTPd/9913FgtGTYuLkwptY3dKnr4gQWvBNEQkqRyWLFmC2NhYPP3001AoFJbOREREMpNUDu7u7ggJCbF0FiIishGSDmX19/dHbm6upbMQEZGNkLTmkJubiw0bNkCtVkOtVkMQBCgUCu5zICJqpCSVw9q1ay0cg4iIbImkzUqtW7fGyZMnkZqaihYtWuD48eNo3bq1pbMREZFMJJXDRx99hM8++wzZ2dkoKytDcnIy3n///XovdP/+/YiIiMCgQYOwYMECAEBeXh50Oh1CQkKQlJRU73kTEdGjk1QOO3fuxKpVq+Di4oLmzZsjNTXVNBCfuS5duoR58+YhJSUFGRkZOHXqFHJzcxEXF4eUlBRkZWUhPz+fO8CJiGQkqRxUKhUcHR1Nt93d3aFSSdpdIbJ3714MHjwYLVu2hFqtRlJSElxcXNCmTRv4+PhApVJBp9MhOzu7XvMnIqJHJ+kTvlWrVvjqq6+gUChQUVGB1atX13ufQ2FhIdRqNSZPnoyioiL06dMHHTp0gEajMU3j5eWF4uJis+br6elarzz1YY9DOthjZno0ZZVGs/7f3TyawVmttGCihmev72t7yC2pHN566y3ExMTg7Nmz6Nq1K/z9/bF06dJ6LdBoNOLo0aNYv349mjVrhilTpsDZ2bnamde/HSprjuvXS1BVJdQ94SPSaNyg19+1+HIakj1ktoc/FnvjrFaaPSSJrb9Pfs8e3tc1sZXcDg6KWr9USyoHb29vrFu3DqWlpTAajXB1rf+39D//+c8ICAhAixYtAAD9+/dHdnY2lMr/fWPR6/Xw8vKq9zKIiOjRSCqHNWvW1Hj/+PHjzV5g37598cYbb+DOnTt47LHH8M033yA0NBQfffQRCgsL8fjjjyMzMxORkZFmz5uIiBqGpHI4d+6c6eeKigp8++23CAgIqNcC/f398fLLL2P06NGorKxEr169MGrUKDz55JOYPn06ysvLERwcjNDQ0HrNn4iIHp2kcli8eHG128XFxZgzZ069Fzps2DAMGzas2n0BAQHIyMio9zyJiKjhSDqU9Y+8vb1x5cqVhs5CREQ2wux9DoIgID8/H56enhYLRURE8jJ7nwNw/7yHmJgYiwSixuFhl/0kIvtQr30ORHXhZT+J7JukcnjxxRdrPSnt008/bbBAREQkP0nl0KVLF1y4cAEjRoyAWq1Geno6DAYDtFp+2yMiaowklcN3332HTZs2mc5iDgwMxIgRIzBw4ECLhiMiInlIOpT1xo0bKC8vN93+9ddfUVZWZrFQREQkL0lrDmFhYRg5ciQGDBgAQRCwa9cuREdHWzobERHJRFI5zJw5E08//TT+85//wMnJCe+88w5eeOEFS2cjIiKZSD5D2tvbGx06dMCrr74KtVptyUxERCQzSeWwbds2vPnmm/j4449x9+5dTJ06FampqZbORkREMpFUDhs2bMDmzZvh6uoKT09PpKWlYd26dZbORkREMpG0z8HBwaHaBX5atWpV7eI8RGT/zL2saGm5ASV3Si2YiOQkqRw8PDxw+vRp01nSGRkZ+NOf/mTRYERkXfW5rGiJBfOQvCSVQ1xcHGbOnImffvoJvXv3hpOTE1JSUiydjYiIZCKpHMrKypCeno6CggIYjUa0a9eORywRETViknZIz549G0qlEu3bt8dTTz3FYiAiauQklUPHjh2xY8cOXL16Fbdu3TL9IyKixknSZqWcnBxkZ2dXu0+hUOD06dMWCUVERPKSVA4nT560dA4iIrIhtW5Weuutt0w/37hxw+JhiIjINtRaDvn5+aafJ0yYYPEwRERkG2otB0EQavyZiIgaN8mjstZ2DWkiImpcat0hXVVVhdu3b0MQBBiNRtPPv/Hw8LB4QCIisr5ay+HcuXPo0aOHqRC6d+9ueoyHshIRNV61lsOZM2eslYOIiGyI5H0OlvDee+8hNjYWAJCXlwedToeQkBAkJSXJGYuIqMmTrRwOHTqEL774AsD9gf3i4uKQkpKCrKws5OfnIzc3V65oRERNnizlcOvWLSQlJWHy5MkAgBMnTqBNmzbw8fGBSqWCTqcTDddBRETWI0s5zJ07F7NmzYK7uzsA4Nq1a9BoNKbHvby8UFxcLEc0IiKCxLGVGtKWLVvQqlUrBAQEIC0tDcD9Q2Z/fx6FIAhmn1fh6ela90QNxJxLKdoKe8xMtk/u95Xcy68ve8ht9XLIysqCXq9HeHg4bt++jXv37uHKlSvVrkmt1+vh5eVl1nyvXy9BVZXlz+LWaNyg19+1+HIakhyZ7eHNT49Ozr8Fe/xbBGwnt4ODotYv1VYvhzVr1ph+TktLw5EjRzB//nyEhISgsLAQjz/+ODIzMxEZGWntaERE9IDVy6EmTk5OSEhIwPTp01FeXo7g4GCEhobKHYuIqMmStRwiIiIQEREBAAgICEBGRoaccYiI6AFZT4IjIiLbxHIgIiIRlgMREYmwHIiISITlQEREIiwHIiISYTkQEZEIy4GIiERYDkREJMJyICIiEZsYW4lsm6u7C1yc+FYhakr4F091cnFSoW3sTrOeU5CgtVAaIrIGblYiIiIRlgMREYmwHIiISITlQEREIiwHIiISYTkQEZEIy4GIiER4ngMR1UtZpREajZvk6UvLDSi5U2rBRNSQWA5EVC/OaqVZJ0cWJGhRYsE81LC4WYmIiERYDkREJMJyICIiEZYDERGJsByIiEiERysRkVWYe+grwMNf5cRyICKrMPfQV4CHv8pJls1KycnJ0Gq10Gq1SExMBADk5eVBp9MhJCQESUlJcsQiIqIHrF4OeXl5OHDgAL744gts374dP/74IzIzMxEXF4eUlBRkZWUhPz8fubm51o5GREQPWL0cNBoNYmNj4ejoCLVajfbt26OgoABt2rSBj48PVCoVdDodsrOzrR2NiIgesHo5dOjQAV27dgUAFBQUYNeuXVAoFNBoNKZpvLy8UFxcbO1oRET0gGw7pM+fP49JkyYhJiYGSqUSBQUFpscEQYBCoTBrfp6erg2c8OHMPeLCFthjZiKg9veuvb6v7SG3LOVw7NgxzJgxA3FxcdBqtThy5Aj0er3pcb1eDy8vL7Pmef16CaqqhIaOKqLRuEGvv2vx5TSkR81sD29karwe9t61x79FwHZyOzgoav1SbfXNSkVFRZg2bRqWLl0KrVYLAPD398fFixdRWFgIo9GIzMxMBAUFWTsaERE9YPU1h9WrV6O8vBwJCQmm+6KiopCQkIDp06ejvLwcwcHBCA0NtXY0IiJ6wOrlEB8fj/j4+Bofy8jIsHIaIiKqCcdWIiIiEZYDERGJcGylRsDV3QUuTrX/V/7+iCMOZkZEdWE5NAIuTipey5eIGhTLoQmqz9DJRHKo6736x8e4VtxwWA5NkLlDJxckaC2Yhujh6vNe5Vpxw+AOaSIiEmE5EBGRCMuBiIhEWA5ERCTCciAiIhGWAxERibAciIhIhOVAREQiLAciIhJhORARkQjLgYiIRDi2kg2SMgQ3EZEl8RPIBtVnCG4ioobEcrACrgkQkb3hJ5YVcE2AiOwNy4GIGg1zL2TFiwM9HMuBiBoNXhyo4fBQViIiEmE5EBGRCDcrEVGTxX0UD8dyIKImi/soHo7lYCZzv2kQUePRlNY0WA5mMvebBsDzFogai6a0pmFT5bBjxw588MEHMBgMGDt2LMaMGSN3JCKienvYmsbD1j5saU3DZsqhuLgYSUlJSEtLg6OjI6KiotC9e3f4+vpadLkc2oKILMXcNY0z74aavdnaUoViM5+KeXl56NGjBzw8PAAAAwcORHZ2Nl555RVJz3dwUNRruS5OKvR+b7/k6Q+80Q+PN3cxeznmPsfep7fGMux9emssw96nt8YybGl6Z7XSrM8j4P5n0r16fP7V9ZmpEARBMHuuFvDhhx/i3r17mDVrFgBgy5YtOHHiBN59912ZkxERNT02cxJcVVUVFIr/NZkgCNVuExGR9dhMObRs2RJ6vd50W6/Xw8vLS8ZERERNl82UQ8+ePXHo0CHcuHEDpaWl2LNnD4KCguSORUTUJNnMDmlvb2/MmjUL0dHRqKysxLBhw/Dss8/KHYuIqEmymR3SRERkO2xmsxIREdkOlgMREYmwHIiISITlQEREIiwHMx09ehQRERHQ6XSYPHkybt++LXckSY4dO4Zhw4YhPDwcY8eOxZUrV+SOJNny5cvx73//W+4YddqxYwcGDx6MkJAQbNy4Ue44kpWUlCAsLAyXL1+WO4pkycnJ0Gq10Gq1SExMlDuOJCtWrMDgwYOh1WqxZs0auePUTSCz9O/fXzh//rwgCIKwZMkS4V//+pfMiaTp27evcPr0aUEQBGHLli3C5MmTZU5Utzt37ghvvvmm8OyzzworV66UO06tfv75Z6Fv377CzZs3hV9//VXQ6XSm94kt+/7774WwsDChc+fOwqVLl+SOI8nBgweFkSNHCuXl5UJFRYUQHR0t7NmzR+5YtTp8+LAQFRUlVFZWCqWlpULfvn2FCxcuyB2rVlxzMFNWVhZ8fX1RWVmJ4uJiuLu7yx2pThUVFZg5cyb8/PwAAB07dkRRUZHMqeqWk5ODtm3bYvz48XJHqdPvB45s1qyZaeBIW5eamop58+bZ1WgEGo0GsbGxcHR0hFqtRvv27XH16lW5Y9XqhRdewKeffgqVSoXr16/DaDSiWbNmcseqFcvBTGq1GmfPnkVwcDAOHz4Mrdb2L+Tj6OiI8PBwAPfHsEpOTkb//v1lTlW3IUOGYOLEiVAqlXJHqdO1a9eg0WhMt728vFBcXCxjImkWLlyIv/3tb3LHMEuHDh3QtWtXAEBBQQF27dqF4OBgmVPVTa1WY+XKldBqtQgICIC3t7fckWrFcniIXbt2ISgoqNq/cePGAbj/zTsvLw9Tp041jSJrK2rLXVFRgdmzZ8NgMGDSpEnyBv2d2jLbCw4caX3nz5/HSy+9hJiYGLRt21buOJLMmDEDhw4dQlFREVJTU+WOUyubGT7D1gwaNAiDBg2qdl95eTn27dtn+tb9j3/8A++9954c8R6qptwA8Ouvv2LKlCnw8PDABx98ALVaLUO6mj0ssz1p2bIljh49arrNgSMt69ixY5gxYwbi4uLsYu39woULqKioQKdOneDi4oKQkBCcPXtW7li14pqDGVQqFebPn4/8/HwA97/xduvWTeZU0rz++uto06YNli9fDkdHR7njNDocONJ6ioqKMG3aNCxdutQuigEALl++jPj4eFRUVKCiogI5OTl47rnn5I5VK645mEGpVCIpKQlz586F0WiEt7c3Fi5cKHesOp06dQo5OTnw9fXF0KFDAdzfJr5q1SqZkzUeHDjSelavXo3y8nIkJCSY7ouKisKoUaNkTFW74OBgnDhxAkOGDIFSqURISIjNFxsH3iMiIhFuViIiIhGWAxERibAciIhIhOVAREQiLAciIhJhOVCjdvnyZXTq1Anh4eEIDw+HTqdDVFQUsrKyJD0/OTkZ+/btk7y8S5cuoUuXLjUOnaHT6bB3796HPrdfv344efKk5GURWRLPc6BGz9nZGenp6abbV65cwbhx46BUKjFw4MBan3v48GH4+vpKXpaPjw969uyJtLQ0TJkyxXT/8ePHcffuXfTr18/8X4BIBlxzoCandevWmDFjBlavXg0AuHjxIsaPH48RI0agb9++mDJlCsrLy7Fx40bk5+cjMTERe/fufeh0fzRmzBikpaXh96cQpaamIioqCjdv3sTUqVMxcuRI9OvXDy+++CKuX79e7fmHDx9GWFjYQ29/8MEHGDp0KMLDwzF16lTTWsqePXswdOhQREREYPjw4fj2228b9HWjpoXlQE2Sn58fzp07B+D+B/eQIUOQmpqKPXv24PLly/jqq68wZswYdOnSBTExMRgwYMBDp/ujwMBACIKAI0eOAADu3r2LnJwcDB8+HDt37kTXrl2xefNm5OTkiNZq6rJ9+3acO3cOW7ZsQXp6OoKDgxEfHw8ASExMxLx585CWloaZM2fi8FcQ2l8AAAJ9SURBVOHDj/5CUZPFzUrUJCkUCjg7OwO4P+7UwYMHsWrVKhQUFODatWu4d++e6DlSp3NwcEBUVBS2bduG7t27IyMjA8HBwfD09MTYsWNx9OhRrFmzBgUFBTh//jz8/f0l5/7yyy9x8uRJREZGArg/GmxpaSkAQKvV4pVXXkFwcDB69eqFf/7zn/V5aYgAsByoiTp58iSeeuopAMBrr70Go9GIQYMGoU+fPigqKkJNo8pInQ4AIiMjERoaipKSEqSmpmL+/PkAgCVLluDEiROIjIxE9+7dYTAYRPNQKBTV7qusrDT9XFVVhZdffhmjR48GcH8Y9t8uVTtr1ixERkbi4MGDSEtLwyeffIKtW7c+wqtETRk3K1GTc/HiRaSkpOCll14CABw4cADTpk3D4MGDAQA//PADjEYjgPuDLRoMhjqn+6PmzZujb9++WLlyJZRKpeniNAcOHMDYsWMxZMgQeHp6Ii8vTzSPFi1a4OrVq7h+/ToEQcDOnTtNj/Xu3Rtbt25FSUkJgPvXJY6JiYHBYEC/fv1QWlqKUaNGYd68eTh79iwqKioa6mWjJoZrDtTolZWVma6E5+DgACcnJ7z22mvo06cPgPvfuKdNm4ZmzZrB1dUVzz//PH766ScA9w8vXbZsGSorK2udriajR4/GiBEjqo3cO23aNCQmJmLFihVQq9Xo1q2baB6+vr6IiopCZGQkNBoN+vTpYzrEdfjw4SguLsaIESOgUCjQqlUrJCQkQKVSIS4uDrNnz4ZKpYJCocCiRYs4PDvVG0dlJSIiEW5WIiIiEZYDERGJsByIiEiE5UBERCIsByIiEmE5EBGRCMuBiIhEWA5ERCTy/yUM1kYHtA7aAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(x,25)\n",
    "plt.xlabel('Data Values')\n",
    "plt.ylabel('Frequency')\n",
    "plt.title('Histogram, 25 bins')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### KDE\n",
    "\n",
    "Kernel Density Estimation (KDE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Density')"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "sns.kdeplot(x)\n",
    "plt.xlabel('Data Values')\n",
    "plt.ylabel('Density')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Cumulative probability density"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Cumulative probablity density function')"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "numbins = 20\n",
    "cdf = stats.cumfreq(x,numbins)\n",
    "plt.plot(cdf[0])\n",
    "plt.xlabel('Data Values')\n",
    "plt.ylabel('Cumulative Frequency')\n",
    "plt.title('Cumulative probablity density function')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Boxplot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Values')"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# The error bars indicate 1.5* the inter-quartile-range (IQR), and the box consists of the\n",
    "# first, second (middle) and third quartile\n",
    "plt.boxplot(x, sym='o')\n",
    "plt.title('Boxplot')\n",
    "plt.ylabel('Values')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Values')"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.boxplot(x, vert=False, sym='*')\n",
    "plt.title('Boxplot, horizontal')\n",
    "plt.xlabel('Values')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Errorbars"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-0.2, 19)"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.arange(5)\n",
    "y = x**2\n",
    "errorBar = x/2\n",
    "plt.errorbar(x,y, yerr=errorBar, fmt='o', capsize=5, capthick=3)\n",
    "\n",
    "plt.xlabel('Data Values')\n",
    "plt.ylabel('Measurements')\n",
    "plt.title('Errorbars')\n",
    "\n",
    "plt.xlim([-0.2, 4.2])\n",
    "plt.ylim([-0.2, 19])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Check for Normality"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Probplot - check for normality')"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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8pG9m8TeViAgPoqM1atbMTAD2XBQGSQJCiLKqBKZ3jozUFemiHhSUYfcX/dwkCQghypwc0zvX8yfho81FbvMvqMvnK6+UUuA2IDUBIUSZ4XL1ChVeGULlTs+g+/ViZtF3/2H0z3YqctHXUbt8FpUkASGEw8tR9P3iM5LHjrNY9LU0utdRu3wWlSQBIYTj0uvxenc1VVo8iteqt0jrHsSt709ye/I0lHcFsxf6rKaeqCgXlNKIinIhNNQzRyJw1C6fRSVJQAjheJTC/bNPqdy2Od5hE++M9F35jqnXT0EX+sI09Thql8+ikiQghHAouhPH8Hm+E5WG9Ad3dxI+3sK/+3zBI4Na5bjjL+hCX5imnqCgDJYsScXPz4imKfz8jCxZYt/zABWH9A4SQjgEl6tXKB8xA8/tkTnW9I3c6UnoG3l78aSk5L+frO6eUVF5E0Huph5H7PJZVPIkIISwK7nb8WeMSWFrrVl4P/4YascX7Go8mYfdL1Jpwus0bV6JsLD87/hdXfPff1Z/f2do6ikMeRIQQthU9gFZPj6K5GQNvV7DDT09otYw/eOZVCaODxhIuJpD9KmawJ27fsi/UGswZF7YsyeIrAu9o47uLQ2SBIQQNpN7QFZcXOZFvTvbWcBE6nGRr2nPOBZzikfN7CX/9n0/v8wLu7kLvTM09RSGJAEhhNVl3f1n3snfuYg34yhv8gZt+Y6zPMxzfM5uAjF3ob9D5fib7Hf8cqEvmM1qAklJSXTp0oWoqChbhSCEsIHsXTezLtz3c5mP6cNRWlCXiwznHRpzit08h+UEAJUrqzLfi6e02ORJ4NSpU4SHh3P58mVbHF4IYUPZu25WIp4pzGU0yzHgyiymsojxJFEh2zty3uXnd9c/d65ztueXBJs8CWzevJnp06dTvXp1WxxeCGFD0dGZRd/XeIvfqM04FvMxfanHBaYzizQ3b6pUuXNXP3hweo67/Nyv5a7/7mhKKZuNgX7mmWf44IMP8PPzs1UIQggr+OgjCAuDq1cUPVx2MM+Yf9H3/vshIgL69bNxwE7E4QrDN28mYTQ6xtwd1apVIDY20dZhWJ2ct/MozDln1QAapvzAB4zjSeO3nOVhOvMZX/zT5u/lpXLc0cfGWiH4u+BI37WLi0bVqt7mf2/FWIQQTuj9mddZl9KXo7SgHhdMRd8vXZ9D05AmHRtzuCcBIYRj2PVhEmnTl7A/6S0MuDKbcBYywVT01YyKGzeSbBylkCQghLhr2fv9e7roGWZcw3Rm3RnpyxyiyVn7K2tTMjsqmyaBb775xpaHF0IUQ/YLvqsrGAzeaBooBT3YzgLjROrya4EjfZ11nh57JE8CQgiLsl/4My/4mf30DQYAjWbqCIsZZxrpm73om5MyTecgNQD7IElACFGg3PP7ZO9Ufj+Xmcdk+rCJG1QnhDWsYygGM5cWPz/FiRPJ1ghbFJIkASGEWZGROkaN8sRgyHlHn3ukb+6ib36kCcg+SRIQQuRgrukHwA09r7CGacyiCrd4n0FMZXaeom8WTVMohTQB2TFJAkIIIPPiP2WKxz/TOedu+lF0ZwcLmWC26Jt1wc8sFsuF31FIEhDCyeV38c8uv+md99AJRVbvICn2OjKLI4Z/++03tmzZglKKMWPG0KFDB77//ntrxCaEKAVZyzdWr+6Nr683I0Z4Ehd3Z1rnLA/wu2l65+wjffe6BrJqdRoxMUlkZEBMTBInTiRLAnBQFpPA9OnT8fDwYP/+/dy4cYOIiAiWLl1qjdiEECUoMlKHv395Roy4M5d/Znt/3qLvQsbzC/Xpxk5mE04dfuVdhuPu5crKlTLFQ1liMQmkpaXx/PPP89133xEYGEiLFi1IT0+3RmxCiBKS1c0zvzv+LNmnd36DN/mIftTlItO1WSThLXP8lFEWk4Ber+evv/5i//79tGrVir/++ou0NOnmJYSjyOrmmX3B9Zwy1/Q9SwPeYjQnaUJTTjCUdaRWuYdVq1KlyacMs1gY7t27N+3atSMwMJA6derw9NNP8+qrr1ojNiFEMeTt4gnm7v6zF31/5qF/RvoGUqUKrI6Qu35nYDEJ9O3bl+DgYFxcMh8atm/fTuXKlUs9MCFE0U2c6MGGDW6mvv3mlox6gN+Zy5RcI32HUKmKK6sjpJePM7HYHJScnMycOXMYNGgQ8fHxLF26lORkGfYthL2JjNTlSAD5qUQ8C5hgKvrOIYw6XOQLv2GsXJ3BL79Ik4+zsZgE5syZQ4UKFbh58yYeHh4kJSUxbdo0a8QmhCikrHZ/cwkg95q+G7W+RM49RUjMRC7FaNLe78QsJoFz584xduxYdDodXl5eLF68mHPnzlkjNiGEGfn19c89v08mRQ+25Sj6tvI4TuqqVXR6uYbV4xb2x2ISyKoFZDEYDHm2CSGsZ+JED159teC+/gDNOcIBnmQbQehx5zk+o3flL3lpWX256xcmFq/mzZo1Y9GiRaSmpvLtt9/y2muv0aJFC2vEJoTIpTDt/lkjfY/QkrpcJITVdK55kudXP8Mv529LAhA5aEqZ6z+QKT09nbVr17J//34MBgNt27bl1VdfxcPDw1ox5nDzZhJGo2MsS1etWgViYxNtHYbVyXmXjLwreGX9Jv8EkHt656VaKDWWvM7z/bxKLKbc5Lu2fy4uGlWrepv9vcUuom5ubowcOZKRI0eWaGBCiLyyX/gzZV/BK39Z0ztPZ6ZpTd8Iz9m8sfRfPC93/cICi0nglVdeyXf7mjVrSjwYIZyRuQu/ZSpzTV+yr+m7iD+qNCZC+vqLQrKYBDp27Gj6OT09nS+//JKGDRuWalBCOIvcSzcWVt7pnT/jTM1OhIXrCQqScTyi8CwmgR49euR5PWDAgFILSAhnEhHhUaQEkHuk7whtNY+u6Mf7LwLcLrU4RdlV5L6eSiliYmJKIxYhnE50dOESQH7TOzfyvEjjVYPo+WIpBynKtCLXBC5cuEDz5s1LLSAhnEVkpA4Xl6IVfd/XBjFVzULzq8ksWclLlIAi1QQ0TaNPnz60adOmVIMSoqzLPdFbXpnTOy/8p+gb5d8ObdVsujR6hC4ASLu/KBlmk0B8fDwA7dq1y/O7xMREfHx8Si8qIcooS+v5gqIZR1npPo7m+u/I8K9PwvQteLQPwKAVrXgsRGGYTQItW7ZE07Kmo80cnKVpGkopNE27q/mDdu3axerVq8nIyGDQoEH069ev2PsSwlFYuvu/n8vMYzJ92ISxUnUSJy4nte8A0Fl8YBei2Mz+6/rll19K5YA3btxg6dKlbNu2DXd3d4KDg2nRogV16tQpleMJYUs57/whv7v/7CN9jZoryWMnkDJqNMq7gnWDFU7J4i2GXq/nf//7n2kNAYPBwNWrVxk7dmyxDnjo0CFatmxpak7q2LEje/bsYdSoUcXanxD2pCireuUp+jII3dwwOg31tWrMwrlZTAJjx47ljz/+IDY2locffphTp07dVe+gmJgYqlWrZnpdvXp1Tp8+Xez9CWEvPvqIHAO/zM/KlXek73gW0WRwAxYMlfW7hXVZTALnzp1j7969zJgxg8GDB2M0GpkxY0axD2g0Gk21BsBUYyisgiZCskfVqjnnI70znvfo0Vgc+JV3pO/nHK0SyPK3NDJLY+5WibUkOeN3DWXnvC0mgerVq6PT6XjggQe4cOECgYGBJCYWf/a8GjVqcOzYMdPr2NhYqlevXuj3yyyi9s/ZzvtOu7/5sZfZi75/4stw3mE9gxkwWHFuQRIAsbHWirjkONt3ncWRztvSLKIWRwyXK1eOXbt2Ub9+fXbv3s358+e5fbv4w9NbtWrF4cOHuXXrFikpKezdu5cnn3yy2PsTwpayFngxlwCyRvqex59u7GQWU6nLBSKrvMyK1RksWCDNP8K2zCaBrN5B06ZN49y5c7Ru3RoXFxcGDBjA0KFDi31AX19fxo4dy8CBA+nevTtdunThkUceKfb+hLCFyEgd/v7lWb8+/y6f2df0fYM3+Zi+1OUCy6vMYNFqN1nQXdgNs4vKtGzZkgcffJABAwYQEBCAzk76KktzkP0r6+ddcH//vEXfRdUX0mPmQ2Xyol/Wv2tzHOm8i90cdODAAfr168emTZto164db731lkwcJ5yapbv/7Gv6puFBN/cv+HXVp/znp7plMgGIssFsEnB3d6dLly588MEHfPjhh6SmphIUFMTo0aP54YcfrBmjEDYTGamjadPyVK/uzYgRWW3/ORNA7jV9h7OGZyqfIHD50wT1KmB2OCHsQKGmkn7ggQeYMGEC+/bto0aNGgwaNKi04xLC5rIWfImKyrrw57z4+xCXZ3rnelwgY/Bgzp6XGT6FYyhUQ390dDTbtm1j+/bt+Pn5sWTJktKOSwibM7fgixt6RrCaacyiMnFs4CWmMou0qn4snJNCUJD0+BGOw2wS0Ov17N27l61bt3L27Fmef/551q5dK3P8CKeRd8EXRU+2sYCJ1OG3f9b0XcxprTEvvZTOv/8NsbFy9y8ci9kk0KZNG3x9fenTpw9vv/025cuXt2ZcQthczZrKtPh7c47wJm/QhoOc5WEC+YI9dKRKFVgVkfpP04/jjfYVwmwSePvtt2nWrJk1YxHCrjz7bAb710cxlykE84lppO+/GYJRc2XwS+ky2Es4PLOFYUkAwllFRupoXlfPQ+uncI6HeJ5PmU04dbnAuwzjHj8XVq1KlQQgygT7GAEmhJ3Y/omRX0Pf4Vj6bNP0zuHM4Ro1AfDzM3LihCztKMoOSQJCACiF++e7eHbsDIZn/Goq+p7i0Rx/lrdYLIRjM5sELA0Ik+YiUVbojv+A9/Qw3I5+T9Q/0zvvJpD8FoOpWdMxpiwRorDMJoFZs2YBkJKSwrVr16hTpw46nY4LFy5Qu3Ztdu7cabUghSgNLlcuUz5iBp47tnG7YnXGuq1hTfpQDGb+s/DyUoSFSR1AlC1mk8CuXbsAGDNmDAsXLqRp06YAnD17ljVr1lgnOiFKgRYfR7llb+L13hpwdSU5dDwPrw/janolM+9QVKmiiIiQUcCi7LFYE/j9999NCQCgQYMGXLlypVSDEqJU6PV4bXiPcm8uQIuPJzW4H7cnhbPl0H1cjfM08ybF6tWpcvEXZZbFuYM8PT3Ztm0bBoOBjIwMNm7cSMWKFa0RmxAlQyncP/uUym2b4x0+iYxGjxK37zuSlq9iy6H7GDXKE3OLwfv5KUkAokyz+CQwd+5cxo0bR3h4OJqm0aBBA958801rxCbEXcte9M3wr0/Cxq3on3kWNM00QZzBYK7Hj9QARNlnMQnUrl2b7du3Ex8fD4CPj0+pByXE3cpe9DVWq07i4uWk9h0A2RZHCgvLf4K4LJUry1OAKPssNgfFxsYyfPhwevfujcFgYOjQobK4jLBbWkI85WeEU6X143h8uZvk0PHcOnKS1IGDidzpaVobwNfXm1u3zCcALy/F3LnyFCDKPotJYObMmXTo0AEPDw8qVqxI/fr1CQ8Pt0ZsQhSeXo/X2lVUad4Yr9UrSA16kVvfn+T2pKko7wp51gbIXBks/yTg6qpYskSKwcI5WEwC0dHRvPjii7i4uODm5sb48eO5fv26NWITwrLcRd+GjYn7+luSlq/CeM+9ppXBRozwLLDpJ9sOWblSEoBwHhZrApqmYTQaTa+TkpJyvBbCVnIUfes/RMLHW9C3DzAVfadM8SAuzvwdf36kDiCcjcUkEBAQwLhx40hMTGTTpk1s2bKFwMBAa8QmRL7yFH3ffIvUPv1NRd+JEz3YsCH/xeALInUA4YwsJoFXXnmFHTt2YDQaOXToEL179+aFF16wRmxC5JB3pO8EUkaNZuuXlYlo7kFUlIamgVJQlLt/GREsnJnFJDBhwgQWLlxI9+7drRGPEHnlM9J3a6NpjF5Um7glWRf7zP9XRZrfTeHnlzkWQC7+wllZTALnzp1DKYWmyRS6wsr+md65/Oxp6H6/hP7JduxsO4+QVc2I21i0tv7cvLykB5AQUIgkUL16dTp37kzjxo1zrDMs3URFacpvpO/GuEBC3/AqZC+f3BQuLmA0Inf/QmRjMQk0adKEJk2aWCMWIdj7ThQV5s7g+ZTN/Ikv01jD+xeHoO+T9U+16AlA0xQvyXrAQuTLYhIYNWoUqaRdatEAABfGSURBVKmpXLlyhbp165KWloaXl9ddH3jZsmW4urry2muv3fW+hOOJjNQREZFZzHV1hQqGOMKYy2uswIArs5jKIsaTRAUodo9kKfgKYYnFwWKnTp2iQ4cOhISEEBMTw9NPP82JEyeKfcDExESmTJnC+vXri70P4ViyBmz5+nrj71+eBx/MHLwVFeWCG+mMNLzFr9QllCV8TF/qcpHpzMpMAEWicHFRZBZ8jaxencovvyRLAhCiABaTwIIFC9iwYQM+Pj7UqFGDhQsXEhERUewD7tu3jwceeIDBgwcXex/CcWSfrkEpjbg4F5KTM//Z9SSSn3mY5YzhBE1pwkmGsN60qHvhZF70q1TJvOj/+WcSMTFJnDghF38hCsNiEkhNTaVOnTqm10899RQGg6HYB+zevTvDhw/H1dW12PsQjiMiIu9Mnc05wre0JZJepOJJIF8QwF5O07hI+3Z1zVzwJSYmSe74hSgmizUBnU5HQkKCqYvopUuXCrXj3bt3M2/evBzbatWqxYYNG4oeZTZVq3rf1futrVq1ojZplA3VqlXgo48gKurOtgf4nXlMJphP+BNfhrGW9Qw2u6ZvQcqVg7VrNfr1u/v6VElyxu/bGc8Zys55W/yvb8SIEfTv35+//vqL0NBQDh48aFqEviCBgYGlMr3EzZtJGI1FGhFkM9WqVSA2NtHWYVhFVqE3OlqjShWN1FQjycmZffl9iGMKc3mdt/IWfc2606XT1RUMhjv/n9XFMyAgg9hYa52hZc70fWdxxnMGxzpvFxetwJtni0mgXbt21KpVi4MHD2I0Ghk5ciS1a9cu0SCFY8tq989q9rl5E8AFN/SMYDXTmEVl4tjAS0xldq42/7wXe+nHL4T1mE0C165dM/3s5ubG008/neN39957b6kGJuxL9jt9Hx+FpkFcXObPCQkaRmP2dn9FD7azgInU5Ve+ogPjWPxPm39mETcuTqNmTbnYC2FrZpNA586d0TQNpRSpqamUL18eV1dX/v77b6pWrcp33313VweW8QGOI/edfub0zOT5GTKLvm/yBm04yE80oBO7+ZKOZA3y8vNTnDiRbLXYhRAFM5sETp48CcC0adNo0aIFnTt3BjK7eH799dfWiU7YVPYBXZZG6ham6OvlJQu3C2FvLHYR/emnn0wJAKB9+/b88ssvpRqUsL3cyzGa40McixjHL9TneT5lFlOpw6+8x7BsCSCzCUgmbBPC/lhMAkajkSNHjpheHzhwQGYUdQL59e/Pzg09r7OcX6lDKEv4iH6mkb63NW+qVDGiaTJyVwh7Z7F3UHh4OGPGjMHNzQ2lFEop3n77bWvEJmwgZxNQfhQ92cZ8JlGXX9nLs4xnkWmgl5eX4t13NQICpN1fCEdgMQnEx8fz3//+lwsXLgDg7++PTlf0wT3CPuXu9ZOcrKHX558Ashd9z7k24DnD53zv0xHNRUOLU6bePv36edlV/30hhHkWr+ZLly6lQ4cONGjQwBrxiFJW0EU/d0+fLNmLvrcrVidx+lv8q09/3tfpgNtWjF4IUdIsJoF69eqxevVqHn/8ccqVK2faLknB8RTU1TM/PsQRRgSvsQKj5sqxwMk8sHIUyrtsDJcXQhQiCZw6dYpTp06xZcsW0zZN09i3b1+pBiaKJ/udfs2aimefzeCrr3RER2u4uIDBYLmon3uk75Zyg+hweBL333MvjjFhhxCisCwmgW+++cYacYgSkPtOPypKY/16N7K6eFqe/DVn0fcrOhDusYjBb/pjvEd69ghRFhWYBG7cuMHatWs5fvw4mqbRtGlThg0bRo0aNawVnyDv3X3WVAu5t9++TT7dOgvXnbcF3/Mmb9CaQ5xzbUCg4Qt+qhlAWLheunYKUYaZTQLXr1+nd+/edOzYkdGjR6PX6zly5Ai9evXik08+oWbNoiz8IYorv7v70FBPjh5NZ9MmtxzbKUZjTV3dJRa4TKKHfgsxLr78r89KHl7Ulw+k6CuEU9CUUvleOSZOnMgTTzxB9+7dc2yPjIzk+++/Z9GiRVYJMDdnm0q6adPy/4zazcnVVRWqfT8/rq6KioY45nlH8HLqSlzcXLk94jVujxoD3ne/XoMjTbNbkpzxvJ3xnMGxztvSVNJmRwz//PPPeRIAQFBQEKdPny6Z6IRF0dH5X+jNt+/nTpA5X1f0TON/Pd/kr8p1GJ68FP0LL3Lr+5PcnhReIglACOFYzCYBMw8IALi7u5dKMCKvmjXz/x7Mrc5ZuXLmVA1ZUzYMHpye+Rojw6ps4WqFh2m9ZTwZjR4lbt93JC1fhfEemRZcCGdlNgm4urpy48aNPNtv3LghScCKwsLS8PLKmQi8vBQDB6bnu33u3DROnEjmxo3MxdYXLEjj9Lv7SWvemrW3XqR8VQ/iN0WSsGUHhoaNrHkqQgg7ZDYJBAcHM2XKFJKSkkzbbt68yYQJE+jbt69VghMQFJTBkiWpOe7ulyxJZcGCtHy3Z+/J43LlMhWGv0TlwPa4/n6JxDffIu6bg6Q/8yzIJIBCCAroHdSnTx+uXr1K27ZtqVOnDhkZGVy+fJmBAwcSFBRkzRidXlBQRr7dNM1t1+LjKLd0MV7r3gFXV5LfmMjtkaOlzV8IkUeB4wQmTpzISy+9xKlTpwBo3Lgxvr6+VglMFINej9eG9yj35gK0+HhSg/txe1K4tPkLIcyyOGLY19eXgIAAa8Qiiksp3D/7FO/Z03C9/Dv6J9uRNGOOtPkLISySOaEdnO7YUbynh+H2wxEy6j9E/KZI0tt1kDZ/IUShSBJwUC6Xf6d8xEw8d27DWK06iYuXk9p3AMhaD0KIIpArhoPR4uMot2RRZtFXp5OirxDirkgScBR6PV7r380s+iYkkNqnP7cnhknRVwhxVyQJ2LvcRd+n2pE0XYq+QoiSIUnAjuUu+iZs3IpeBnoJIUqQJAE7lL3oa6juS+KSFaQG95OirxCixMlVxY7kGOmr05E8bhK3X31dir5CiFJj9SRw/Phx5s2bR3p6Oj4+PsydO1cWqNHruTDm3zSInIeniueTci+RPnUKnYbK6GwhROkyO4FcaRk/fjxz5sxh586ddO3alTlz5lg7BPuhFO67duDepDmtt07gqHqcJpykz+1/EzKrNpGR8qAmhChdVk0Cer2e0aNHU79+fQD8/f25fv26NUOwG7pjR/HpEkCloQP5M86LTuymI3s5TWMgc63giAgPG0cphCjrzC4vWdqMRiMjRoygUaNGjBo1yhYh2MalSzB5MmzeDDVqwOzZuA17iYx8WuY0DYxGG8QohHAapdbesHv3bubNm5djW61atdiwYQN6vZ5JkyaRkZFBSEhIkfbriGsMR0bqWDn7NoOvzWUUmWv66rON9K0x04WoqLzvr1nTSGxssvUDv0uOtP5qSXLG83bGcwbHOm9LawyXWhIIDAwkMDAwz/bk5GRGjBiBj48Pq1evxs3NrbRCsAvbPzHyW+gaDqTPxod41jOYea4zGVenKkHemWsBhIWlERrqSUrKnf7/Xl6KsLA0W4UthHASNikM33///SxbtqxsL1OpFGzdSsCYx1iUHspxHqMJJ3mZdfyW6pejvd/c6mH5LRgjhBAlyardT37++Wf27dtHnTp16NGjBwDVq1fn3XfftWYYpS5rpC8/HCGJBnRiN1/SEbhzpx8dnXPUr7lVwoQQojRZNQk8/PDDnD9/3pqHtKrcI3159126zBjIlei8Tzw1azpGXUMIUbZZvTmoLNLi4yg/bQpVWj+Ox1d7SB43iVvfn4SXX2ZSuAEvr5wXfGnvF0LYCxmNdDcKMb1zZhNPKhERHkRHa9SsmZkApOlHCGEPJAkUh1K4f7YT79nT70zvPCMCQ4OG+f65tPcLIeyVJIEi0v1wJHN652NHyXjoYeI3bSP9mQ62DksIIYpFkkAh5Tu9c5/+4Opq69CEEKLYpDBsQX5F3/WTz/DwklfxvbcSTZuWl4nehBAOS65e5uRX9J0UzpaD/5djdG9UlEZoqCcgg7uEEI5HngRy+2d65yptmuE9dTIZjZsQ981Bkpa9jbHGPUREeOSY3gFkxk8hhOOSJ4FsClP0zT3S19J2IYSwZ/IkQGbRt8Kwl6jc+Vlcrl4hcckK4r45mG+vH3MjfWUEsBDCETl1EjA30je1/yCzvX7CwtJkBLAQosxwzuagQoz0NUdGAAshyhLnSgJFHOlrjowAFkKUFU6TBLKmd3b74YiM9BVCiH84RRJwPfczlZ/rICN9hRAiF6dIAobadUhY9wH6dh3A2/xam0II4WycIgng7o6+a3dbRyGEEHbHqbuICiGEs5MkIIQQTkySgBBCODFJAkII4cQkCQghhBOTJCCEEE5MkoAQQjgxSQJCCOHEJAkIIYQTs3oSOHbsGD179qRr16688sorJCQkWDsEIYQQ/7B6Epg8eTILFy5k165d1KlTh3Xr1lk7BCGEEP+w+txBX3zxBW5ubqSnp3Pjxg38/f2tHYIQQoh/WP1JwM3NjfPnz/PUU09x5MgROnfubO0QhBBC/ENTSpXKCum7d+9m3rx5ObbVqlWLDRs2mF5v2rSJHTt2sGnTptIIQQghhAWllgTyk5aWxrfffkuHDpkret2+fZvWrVtz8uTJQu/j5s0kjEarhXxXqlWrQGxsoq3DsDo5b+fhjOcMjnXeLi4aVauaX0fFqs1BOp2OmTNn8tNPPwGZTwtNmza1ZghCCCGysWph2NXVlaVLlzJt2jQMBgO+vr5ERERYMwQhhBDZWL130OOPP862bdusdrzISB0RER5ER2vUrKkIC0sjKCjDascXQgh7VqaXl4yM1BEa6klKigZAVJRGaKgnkCqJQAghKOPTRkREeJgSQJaUFI2ICA8bRSSEEPalTCeB6GitSNuFEMLZlOkkULNm/l1JzW0XQghnU6aTQFhYGl5eOS/4Xl6ZxWEhhBBlPAkEBWWwZEkqfn5GNE3h52dkyRIpCgshRJYy3TsIMhOBXPSFECJ/ZfpJQAghRMEkCQghhBOTJCCEEE5MkoAQQjgxhysMu7g41kAvR4u3pMh5Ow9nPGdwnPO2FKdV1xMQQghhX6Q5SAghnJgkASGEcGKSBIQQwolJEhBCCCcmSUAIIZyYJAEhhHBikgSEEMKJSRIQQggnJklACCGcmCSBUnT8+HF69epFt27dGDRoENHR0bYOyaqWLVvGihUrbB1Gqdu1axfPPfccAQEBfPTRR7YOx6qSkpLo0qULUVFRtg7FKlauXEnnzp3p3LkzCxcutHU4JUKSQCkaP348c+bMYefOnXTt2pU5c+bYOiSrSExMZMqUKaxfv97WoZS6GzdusHTpUj7++GN27NjBJ598wq+//mrrsKzi1KlT9OnTh8uXL9s6FKs4dOgQ3333Hdu3b2fHjh2cPXuWr776ytZh3TVJAqVEr9czevRo6tevD4C/vz/Xr1+3cVTWsW/fPh544AEGDx5s61BK3aFDh2jZsiU+Pj6UK1eOjh07smfPHluHZRWbN29m+vTpVK9e3dahWEW1atWYNGkS7u7uuLm5Ubt2ba5du2brsO6aw80i6ijc3d3p1q0bAEajkZUrV9KhQwcbR2Ud3bt3B3CKpqCYmBiqVatmel29enVOnz5tw4isJyIiwtYhWFXdunVNP1++fJndu3ezceNGG0ZUMiQJlIDdu3czb968HNtq1arFhg0b0Ov1TJo0iYyMDEJCQmwUYeko6LydhdFoRNPuTNWrlMrxWpQ9Fy9eJCQkhAkTJvDAAw/YOpy7JkmgBAQGBhIYGJhne3JyMiNGjMDHx4fVq1fj5uZmg+hKj7nzdiY1atTg2LFjptexsbFO0zzijI4fP87rr7/OlClT6Ny5s63DKRFSEyhF48eP5/7772fZsmW4u7vbOhxRClq1asXhw4e5desWKSkp7N27lyeffNLWYYlScP36dUaOHMnixYvLTAIAeRIoNT///DP79u2jTp069OjRA8hsL3733XdtHJkoSb6+vowdO5aBAweSnp5Or169eOSRR2wdligF69atIy0tjfnz55u2BQcH06dPHxtGdfdkZTEhhHBi0hwkhBBOTJKAEEI4MUkCQgjhxCQJCCGEE5MkIIQQTkySgLCaOXPm0K1bN7p160bDhg3p2LGj6XVqair+/v7cunXLJrENGTLEdOxhw4YVexK4SZMmsW7dupIMrchWrlzJ119/DcDy5cvZsWMHgE0/X2G/ZJyAsJrw8HDTz8888wyLFy+mUaNGNozojoMHD5p+dvSxHEeOHKFOnToAjB492sbRCHsnSUDYlRUrVnDq1Cni4+MZOnQo/fr1A2DLli1s3LgRo9GIj48PU6dOpXbt2iQmJjJz5kx++eUXNE2jbdu2hIaGotPpaNiwIe3bt+eXX35h8eLFlCtXjoiICOLj4zEYDAwYMIBevXoxefJkAAYNGsTatWvp168fy5cvp1GjRmzdupX169fj4uJC5cqVWbBgAb6+vsydO5dTp06RnJyMUoo5c+bw2GOPmT2vGzduMGnSJGJiYrj33ntxdXUlICCAnj174u/vz+HDh6lSpQqA6bWPj4/Z40yaNAlvb2/Onz/Pn3/+ib+/PwsWLGDHjh389NNPLFy4EFdXV/bt20fdunUZOnRojnjMfZ7Hjh1j/vz5GI1GAEJCQujYsWNpfNXCXighbKBdu3bq9OnTObbVq1dPrVu3Timl1NmzZ1XDhg2VXq9XR44cUX379lW3b99WSin17bffqk6dOimllJowYYKaPXu2MhqNKi0tTQ0ZMkS98847pv1t375dKaVUenq6eu6559RPP/2klFLq77//VoGBgerkyZOmv71582aO2M6dO6datGihrl27ppRSav369Wrq1KnqxIkT6rXXXlMGg0EppdQ777yjQkJClFJKTZw4Ub333nt5zjckJEQtXbpUKaXUpUuXVOPGjVVkZGSeY2d/bek4vXv3VmlpaUqv16vu3burrVu3KqWU6t+/v9q9e3eeeLL2W9DnOXDgQPXZZ58ppZQ6d+6cmjFjhuUvUzg0eRIQdqVLly4APPTQQ+j1epKSkti/fz9XrlwhODjY9Hd///038fHxHDhwgI0bN6JpGu7u7gQHB/P+++8zfPhwAB5//HEgc+rfq1evMmXKFNM+UlNT+fnnn3n00UfzjeXw4cO0adOGe+65B4CXXnrJ9LtKlSqxadMm/vjjD44cOUL58uULPK8jR46Yjv3ggw/SqlUri59FkyZNCjxO27ZtTXNS1atXj4SEBIv7BAr8PAMDA5k1axbffPMNrVq1IjQ0tFD7FI5LkoCwKzpd5j/JrOmYlVIYjUa6devG+PHjgczpm2NiYqhUqVKeqZyNRiMZGRmm1+XKlQPAYDBQoUIFdu7cafrdX3/9RYUKFczG4urqmmPfqampREdH88cffxAREcHgwYNp3749tWrV4tNPPy3wvDw8PFDZZmgxN6OsXq83/bx///4Cj+Pp6Wn6WdO0HPsvSEGfZ3BwMO3atePgwYN8++23rFy5kj179uDh4VGofQvHI72DhN1r06YNn3/+OTExMQBs3LiRQYMGmX73n//8B6UUer2ezZs353uX/eCDD+Lp6WlKAtevX6dLly789NNPQOYFP3vyAGjRogWHDx82HXfTpk0sWrSIgwcP0q5dO/r27UvDhg35+uuvMRgMBZ7D008/zaZNmwD4888/OXz4sOl3VapU4cyZMwB89tlnpu3FOY65c8muoM8zODiYc+fO0bNnT2bPns3ff/9NbGysxWMKxyVPAsLutWnThmHDhjFkyBA0TcPb25uVK1eiaRrh4eHMmTOHrl27kp6eTtu2bXnllVfy7MPd3Z1Vq1YRERHBe++9R0ZGBqNHjzYVczt16sSAAQNyrIbm7+/P+PHjefnll4HM5QXnzp1LUlISb7zxBl27diUjI4PWrVuzd+9eUzE1P5MnT2b69Ol07dqVqlWrmpqYILPX1KxZs6hYsSKtWrUyrVQWHBxc5ONAZs+rJUuWkJ6eXuTPc9y4ccydO5dly5ahaRqjRo3Cz8+vwOMJxyaziAphA1m9bnr27GnrUISTk+YgIYRwYvIkIIQQTkyeBIQQwolJEhBCCCcmSUAIIZyYJAEhhHBikgSEEMKJSRIQQggn9v/TGdBNRbgQqgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visual check\n",
    "x = np.random.randn(100)\n",
    "_ = stats.probplot(x, plot=plt)\n",
    "plt.title('Probplot - check for normality')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## 2D Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Y')"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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P3B3WNaNB0QTzvQebnUu5dFITY2XHRuv0Xp4g5w+DwEQ1lAbszJTdISXzGZGDZsQQC3RGQ0aL3OxeyTm/UofDC4Jg2Ngw0NwRrgCIakjN+sSI9KFTy8UiJXOvrkmKZNBqc5XRLiQpn7zSA96lXDpFu46Lfl4PJcxAc0doAIhqiCkNKZQ8dFJKUE0Xi5SiW/TgkKDfldtcJVaxXKmiMYMLSQ2fvJhLRyqrSA8lbFRWmZmhASCqIaY0Mgd0w85j50N+6OSUoJq+XClFN+XO3kFrwwfbXBWuojGLr1oLn7yRSthKJcD1ggaAqIqY0ri9d5eQHzo5JRhuhkkoMnuQu56US6GnT6G1cBRNNPuqjVbCZi8Brjc0AERzwnno5JRguBkmoSK3CgHQoWAcIL25KhSi3VdtNiVsdLzFSJgFREyJXBkIvTJMpFYhq8uqsHL3CVxtbPX7W2pCjOIgqRxS/bOzr1or7F6EjgaAmBI5JRhYJ8eTlljf1HFGDrQ/1OEUMpNahdQ1tYkGujvFx6oyc5Tqn11mpXoSDSm7kUAXEDElwXzFoWSYAPC+H4pbKJS0VkBdH73Z3CTRSjTHW5RAA0BMS6hKUGkaqtKMGqmMlYRYZwf3DxA9Pno7IWXkUxJikLvuUNTHBWgASNQgtmqQmsErmeFJrUIARJzKaOfAo5kQM/KxDsDV4kZdU+irRi3Q8l7R3AA0NDRgxowZWLt2Lfr06QMAWLx4MUaNGoWpU6dq3TyxGYGrhtx1hyLKqJFbhYT7UJphoxdpR8zIu1raOqzwjKoZpPW9oqkBqKiowPPPP4/q6moAQG1tLYqKinDgwAGMGjVKy6YJAaDdxqNIfPRm2ehF2vH8lp5zEO557XPRzxkRF9D6XtHUAGzZsgVFRUVYvHgxAKCkpAT3338/unbtqmWzhHiRc+MY5eO1e+DR7JhpH4bW94qmBmDFihV+r+fMmQMAOHLkSNjX7N49OSKZIiEtLcWwto3E6v2elZaCWWMHeV9v/+o0Cn/ztXcj17n6JqzaU4XUlCRMubO393Na9btX1yScFqk22qtrkq5jvf2r03j1k+M4c8WFXl2TsOjBId7+W/03D5e0tBQUTBrqd38A7eckF0waquq4SI2/7/tOhwNtQsfCUmrdK5YLAl+82AC3WKUtjfEsD+1GNPa7eFdlh128rpY2FO+q9B4Co2W/80f3E3VL5Y/uh//3eZUuweFA3/LpKy4UbDuKunoXZo0dZInfXO3gqOc3z+yTisIHBnW4dmafVNXGRWr8v/j2nF/tLDHl77lXlMridDokJ86WMwCERIrRLhil2UVaBoflfMu+qyUzUlpZi9VlVajz2fin9lgZdTjNR0fPiVaSdToAQYD1soAIMRtm8PGKKZjcdYd0Cw4bbQTDRe6sYisF0uUqyYohCMDvF45VXQ6WgiCmQ+yULTUxa60dPZWyXK0lMxPsrGKzGzAPUuPsdIT2+UjRZQWwd+9ev9fFxcV6NEtkMOtGJLXynuX6Z3RJYilSE2NFdxinJob/mEqNg1UPRwmm4M1uwDxIjX/2sB5hnZ8RLnQB2RAzb0RSI+9ZSf/MWGtHEAn4yb0fDCXjYDYjGAy53d1WMGAe5MY/nPMzwoUGwIaYeSOSGm4QPfrnmVmfq2/yHgHZM8KHVaqaqdT7wQg2Dr5G0NOfol3H0atrEvJH9xPth9ErR6l6T10SY7Fw3EDD799QkJqE6Dk5oQGwIaGcqFUwaag3NVIMtRWCGgFarX3pgTNrT+Au0pWU2sFppeMglpIo1g8zrBytunIxKwwC2xAphZKSENPhcIzC33wtGYTV4jANNQK0Wgc45QKRkdSSVzs4rXQclNbEN0vt/Kyh6SiZOxK/XzgWJXNHUvlHAA2ADZFSNA6Ho8MD7mppk3zAtVAIahyGEooiDSfjKNhKItyVhtoHwSgdh2ArBc8YRVJZlZgTuoBsiNQyumjXcdHPh+pSiVQhROoDVeomkHNpzJLZZh/soJhIVhpq+n+VjoNUfwQA498qx7WmVrTKxKGtknlDOkIDYFNCOVFLzpVg9IYqKZQoUqW7YQPjHJkDunVI1fPgmWHLxUb0DKQqGQe5g3TE0lLFvk+sCQ0A8SKmCJLiYiQfcCvmkvsqX6lJre8KRmyVsPPYeWQP64H9f7osmgUESJd0kPtbuEYgUoPiu1II5QjMwO8bnSFEQocGgHgRcxnIZQFplZGhlSKRKyPgi+/GK6lVwv4/XUbJ3JGi35cq6bB813GvsQj8m5IUVbFxAUIzKFJj6/l3z2ufSxpGMXreWO2ZIUOIhA4NAPEj0GUQrCqm2jnLWiqSYGUEPFxrasX2r04js09qWHEOub9J1XoJFjeRGpf4mI6BeymDomRsg8U3fPFd7Zl5bwmRhllAxFRomWqoNDjdKgCvftIeEA8npTScGIgAyGYhSY1LncQmMbG+KhnbeWP6Iykuxu8zcU4HUhPa3/PUqgnMULJqcTm7wxUAMRVaKpJQZrdnbhzYEk6cQy6oKofcaifU/osZISVjmzU0HakpSSjeVRmSC87MCQFEGhoAYiq0VCShKOZeXZMAhBfnUBJUFYsFANJuE6lx6ZIYi6ZWtyIDpXRsp9zZW3b3txhGJwQwAB0eMS+88MILRgsRCi5XM8KsjRURnTsn4Pr1Zv0bNhi9+92tUxwOnLyMVh/tmBjrxE/GDcQtaZEdB3pLWjJ+0CUB/3niouznEmOdKModhn6pCd7vPTKiD54cfTMeGdFHkRye7/Ttlijan2UTB+MzCTmuNbfhydE3+70nNS5Lxg/CmIHdUXmuAdea29AzJQE/kaiJo3Rsw/nNPWOrRA618cQ2rtxIWW1obsOBk5fxgy4JId0z0fqMOxwOdOoUL/43IdxSgwbBIyH1xYh+az2bk9vV6knlVPNYRKn+SMnhdAAvZA0RDeJGOi6+10hJiIHD4UBdY6vf9ax2r0uNY8+UBMlMLTGs1m+lyB0JSQOgkGi9OXxRohyiAbF00MRYp19QU4/fWy4tNVAePdr2tGmVM4E9SKWuOhDaKVrR+ozzTGASlECFoOV5q0ZjloqSnvZeKD0e9t6AYIjtYvZsYAvEKmcCB8IAdPjQAATBLsGlYDnyoSgkK4yZGQ6E8YxTuHsDlFw/MO9/W8U52e9YMW3T6AC0laEBkMFOuxuVPPjnbpwXINd3O42ZFEoMoJJdyZHOYJVufFOzTSMwy4rOitAAyGCn3Y1Kc+SDKXM7jZkYSg1gMOUcOIMNZ1UV6mzeyrNmM6zorAh3Astgp92NYrXjxQi2K9dOYyaG0p3McuMRuMs23IN3QpnNR3r2QCSEcyYDUQeuAGSwU3ApcBmdkhATUpkBD3YaMzGUHK7y9hfVkgXXxFIXla6qQilb7UHrbKNgROoytEK8ycxougJoaGhATk4OampqAADl5eXIzc3FhAkT8Prrr2vZtCpEekSf1WY2vkftlS3I9FZ6DEROmat9rKHVkBqb1MRY3L9mP5bvOi7papMaJyWrKrFVgqdste8JY9Nu76nKiWNq3duR1H7S4khSu6GZAaioqMDMmTNRXV0NAGhsbMTSpUvx9ttvY9euXfjDH/6A//qv/9KqeVWI5Ii+aLg5xQqDAUDmgG7yX/TZWuIAkD2sh+SYWc1IBkPMAMY42g9WkVpRAfL3lpKCdHJlq+eN6Y/0lATU1jd5X0dynq6a93YkLkOjzyiOhntXMwOwZcsWFBUVoUePHgCAo0eP4uabb0bfvn0RGxuL3Nxc/O53v9OqedXwzIpPFmeH9MAYfXOqQdbQdEwb0bvD+zuPnRe92Usra/Fi6XE0tn1vAAQAH39dK/l5qxvJQAInDV0SY9EWZN+iA5C8t0ora3G9ueOpXIGrBSmF6RlTNcdYzXs7nGqrHoyMN0XLvauZAVixYgXuuusu7+vz588jLS3N+7pHjx6orbXWYIVCtARD//PbCx3ek3rY3/6iWvTs2Ba3gOW7jneYJYWjSKww6/J1pYmtoAKRUnYeJRO4cuiSGNthtSB1DacDqk9E1Ly3I3EZRmI8IiUaJniAjkFgt9sNh8PhfS0Igt9rpUhtadaDNJmDwgPp1TUJp2+UFA58P5TrGM0ZkT4A7Q97YD+CKYBz9U1YtacKqSlJmHJnb1lFIjZG2786jVV7quBqaRO9ntoo/Z22f3Uar35yHGeuuNCraxIWPTjEK0+wMUmKi0HBpKGibb2z/rBoADc5Ma7Dbt2CSUNR+JuvvWPjubbva19q65uwv6ZOUm65vqt5b89KS0FqSpKkHHJI9VlqPJWg9Huh3rtmRTcD0LNnT1y48P1s8sKFC173UChYpRZQ/uh+orsT80f3s1S9EamHPT0loUM/lOwlcLW0oXhXJTL7pCI1MVb00PHUxFjRMSreVdlBofleT02U/t6BWSynr7hQsO0o6updyBqaLjsmXRJjsXDcQGT2SRVtS8r4nrni6vD5zD6pKHxgUIeMGKmS1CkJMSjYdlRU7mC1gNS+tzP7pCLzibv93lNyHak+S41nMEJ5xuWy3cz2fJuiFtDtt9+OkydP4s9//jP69OmD3/72t5g2bZpezeuOGrsTzZDitujBIX6KApBeos8b0x8vlh4XdQP54pk9SdUh9LxfWlmL1WVVssFT3+sZMV7BUjSlziCYdntPFIwfLHvtUFNqpTZDiSlrh8OBxlb/cVVaC8hMO2+N2gAWLeUndDMACQkJKC4uxtNPP42mpib87d/+LSZOnKhX84YQyc1plpIKU+7sjbp6l6KH3fPeyk/+6BcIDsSjwOolFHt9U5s3oBzMmHiuF/j5c/VNeLH0uJ9cWhDMHx6JslRDyUi1X7TreEj9EbuunfPtzWQEI0FzA7B3717v/zMyMvDxxx9r3aQfZphFh4OZSiqE/LA7HIDEVidfBSY3w5UKKEtdb3VZVYfPtwrA6rIqTcdLySw9XGWZNTQdFaev4qOj5+AW2gO6cim1ctcJ/I6Ua8guG/bUIBqMYFSXgrByqpZVs4jkatwE5rrLZYAE62fgvgwpN1Ew91GkaLnxrbSyFjuPnfdWC3UL0im4oWL3DXuknaguBWGmWXSoWLWkgpTi9uS6+yK3jJY7TzfUk560REtXgJb3b7S4MEhkRLUBsOosGlDu/zWbi0utwKVUQDnO6RAdAym6JGpzi+sx7lrfv9HgwiCREdUuICM3ikSKkjIUZnRxqeVayBqajuVZQ5Ca8P1GqqRYBzrFx6AoYFOZ3OabheMGhtSuEvQadyvfv8QaRPUKwOqpWsFmaFq5CHxnt726JiF/dD/F11PTteDbf4/S9ewb8M2KkpsRazHD1cu1KHf/mm3lR6xJVBsAvfycWj2Mwa6rhYtAbGNTqOmnWrgW5JSulNtJqppppOjlWpS6fwGYIkWYWB9JA/DGG2/gmWeeCatcg5nQ2s+pVb6+kutqESg2a+BcTulOvb1nh7NuI4mXlFbW4p31h3Hmikv0c3oG6MXu39x1h8L6jbhqIIFIxgAOHjyIWbNm+ZVvIB3RqiiUkutqkcpn1sC5XJ39ncfOd3g/MF9eid++tLLWW7P/9BWX5OeMTqEM5zcyY7xITaxQJNCMSBqAjRs3IiMjA9OmTcO+ffv0lMlSaKUwlVw3kvMKpDBr4FFK6QqCILrvYP+fLvu9DmZQpSpvBn4O0GbcQyGc3yhaqleKEe3GTUskXUBOpxPz5s3D3/3d3+H5559HWVkZ+vXr5/377NmzdRHQ7GjlDlB6XbVdXKEEzvV0KURa0iCYQQ12SHvg941MoQwnucGsKzs1MKvb0goEDQI7ne2Fo6qqqtDY2KiHTJZCq0wjozKYAhWtVBaQEbWKIilpEMygBlOEKQnB6/rrRTjJDVbdWKiEaDZuWiNpAARBwNq1a7F+/Xo899xzePTRR/WUyzJolWlk5E5NX0UrVSJX71mX1Gojc0A3RQHgYAY1WCnrcJMhtFolhboCsXpKtBzRbNy0RtIAzJgxAy6XC5s2bcLgwfJla+1OoLL2+FXVMAJGLmHlsmG0nnX5Ks7UxFhca2r1q/S5cvcJVJy+qigADAQ3qFJlmz3UiZxboKQPZknXjObSD9Fs3LRG0gAMGzYMBQUFiI+P11MeS2KmB10tgvUp1FlXKCbm0ecAABKLSURBVDPhwLbFDo1pbHV7q2QGEhgA9iBnUD3vv1B6XPSa4cwmzeKbDhz7/ztpiGXvSzGi2bhpjaQBWL58uZ5yWBqzPOhqEs5BJ3LB4lAMZLCArAepg+HCXYV4zzNQaTZpBt90NE5OxDB6tWxVononsF6Y4UFXm3APOgHaNyoFHksYioFUOm5Oh7gRiMT365HnnfK/SG4EU4oZfNPRODkh6kEDoAJmeNDDQc4tE85BJ1KzTanZvJSiV3K2cGKsE9nDemDnsfOq+36zhqYHPRdXCUb5pn1/V6kzdaw8OSHqEdXVQPXC6J2h4RBs80w4fZKabTolEmikDKRY23FOBxJjvr9QU6sb2yrOIT7GgS6JsX4bsgCYYleoERvGAn9XKTzHaJphnIhxcAWgAlYMQgVzDQRzhYitHqRmlW6h3XgonQmLjWfmgG43Mn7a1ZpHudU1tSEx1ukNbBrl85ZaTentm1YSP0mMdSJzQDfJcZqVlqK5nMQcOARBUHDyqnm4eLEBbqnon4ZI5cNblXte+1xyhtgzJcGryAomDUVmn1S/vwcqWaBdqcTHOERLKfT0iQWEYyBLK2sls3N82yiZOxK56w5JVgYN5RSxUH5vqfHQszyEB7nf1QH4xWWkxung/xkfVfe6UqLtGffgdDrQvXuy6N+4ArApcn52z/vn6ptQ+JuvUfjAID9FJrV6SIiNlZzphzsT9ijXYDbfs/owIiBvpkCrXGlsXwOotIQGiW4YA7ApYn52MVwtbR0KhkkpibrGVtV93kpTQj3xBCOK2ZkpC0xp7MasRf+IvnAFYFPE/OxSK4JARSaXIaS2z1uJEvVVcHpk3gT6+1MSYkRdX0YoU6XxKO6eJYBBBmDdunXYtm0b4uPjMWnSJDz11FNGiGFrxIKWSgur6ak8gqWE9gxQcFoH5MWCzHFOB2Id8DvA3khlqsQIWzFxgaiP7gagvLwcJSUl2LZtG5KSkjB//nzs3r0bEyZM0FsU2yKVKSOWV58UF9NBkempPKSMjZxrScvMGzGXVItbQJfEWCTFxZhemZr1VDCzyhXt6G4AvvnmG9x7771ITm6PSo8ZMwaffvopDUAIRPqwSAUt9//pMpZOuMXv2mJZQEB4SjYcuc02U5WLf3w6f3RY19RL+Zm1LIRZ5bIDuhuAYcOGYeXKlcjPz0dSUhL27t2LUDJRpdKZ9CDNBPnR2786jVV7quBqafc5n6tvwqo9VUhNScKUO3sruoZc0HLW2EGYNXaQavJ6iETuWWkpsjJt/+o0Xv3kOM5ccaFX1yQsenCI4rGQQ+z37tU1CaevuETfD+f+UOP3VMo76w+LGv53yv/SYXz1vNdDkUtrzPCM64nuBiAjIwNTp05FXl4eunbtioyMDFRUVCj+vt33ARTvqvQqCw+uljYU76oUnamLIRfEDeyjWv1WQ24xAmePp6+4ULDtKOrqXRHNHqX6nT+6n6hLKn90v7DGSatxEeOMiOHyvO8ru973ulK5tMYsz7jayO0D0D0NtKGhARMmTEBJSQnee+89xMfHo2/fvnqLYVnUSDk0onSFVqmSep91q3Z5B6n+B6uFFA5mTf00q1x2QHcDUFNTg3nz5qG1tRX19fX48MMPkZWVpbcYlqK0shb3r9mPu2V2eYbysPgqMaC9qqZHaWpVD0arh9yIHPysoekomTsSv184FiVzR0a00pDrv9q/hVlrVplVLjuguwG49dZbMWHCBDz00EOYPn06HnvsMYwYMUJvMSxDaWUtXiw9Lppn7iGch8VT0z8x1undZRtYEE6pfEoKimn1kFt99ijX/9VlVaq2ZURxulDlAvSZkJB2bFcLKNyMi0j9g+G2K1XbxkNgHnwoKKmbI9fvUGvgaJHtolUdHj39wXe/9rnk3w4vHKuLDL4Y5Qs3uqaSHWMAttoJbGSlyHDblXNlOICQCpwpvbZS90moNXB8U0c9xqBo1/GIjIHZ0kS1JNpz5c1UU8ku2MoAGHWDRdKu3E5YAe1KIVzZIz3IJhwDUlpZi9VlVX4urUgNsV4ll7VSwF0SY0XPPe6S+P3jaYdceTPVVLILtioGZ9QNFkm7wXzkHp99OId7ROKXL62shSPEg148SkwsnqFl5o4aBDtAJxIWjhuIuIBTc+KcDiwcN9D7Wu9sJyOwejzHitjKABh1g0XSbrDZXWOrG6/t/S4s5RRuUFCuRLOcAQlW2dPMMz0tFXDW0HQsmzjY73dYNnGw3+9gh9kxs4H0x1YuIKMqIEbabs8gBdHE3AdKXUzhuE+kFLnTAVkDEkxZmWGmJ+Xm0VoBy/0OntWWWLqGGcZMLewUzzELtjIARt1gkbYrZkCUoNXsUOq6giC/YpGLZ5hhpifnZ480XhKpTKGutqyK3kdo2h1bGQDAuBssknY933tt73eis30ptFJO4SpDKUPWJTEWC8cNNPzBl3PzhLuK06JwHxB8tUWIEmxnAKyKx4B4FEqwUgFazg7DVYZmX+LLuXnCkV2NzJ1wV1uEKIEGwGJ4DIHcBrFINof5UlpZi3fWH8aZKy4/hReJIldjBaZVOmawlU2osquRdmyU64nYAxoAiyI1M4x0c5iHYLNXo1xpaufD+xqTlIQYxDkdaPFxuEeyklKrcB+PbiRaQQPgg5V2Wmo9MzRi05yS8VdTrkBjUtfUhlhHe0yirrE14nsgVWKDV6iF+wDzus2ItaEBuIHVdlpqPTOUK1Ocu+6Q6kpI6firmY4pZkxahfZjMCteeDDi2k/Xmjoq/zinI6zCfWa8B4n1sdVGMDmsttNS68qOcrNUNXfBelA6/mpu5tMyt//tL6r9Don30OIWWOWSmAauAG5gxZ2WWs4Mg+09UNsdpHT81Vz5aOlGk7tvzL66JPaBK4AbsA6JP54VRu+uSZKfCcU4BqtVpHT81Vz5aFl6INh9Y+bVJbEPXAHcgNkWHckamo5ZYwdh1IpPFc2UpYK4Svz7oYy/WisfLQOsSnZvh7u6tFKyggcrymwHaABuwGwLaZQoZzklryRzx8gyHVq04dsfqf0a4awurZasAFhTZrtguxPBwiUaTwtSMivz9DvYZ+VOF6u9UaU0EAeA3xtw4pUS1Py91TzpSo8NgGrf60pOnjMD0fiMAzwRjIgQ6qws2ExZLm00McaBxraOJiAlISYc0S2HmqsbKwaXrZhgYRdoAGyK2hu95Cp9iil/AHBInSgjgZX9yGq5muTGGTDnEYosZ2FemAVkU9SelYll1ASjLoTKplqeyGUllIyz2WbWPOjFvNAA2BS101496ZlqyCCG1TbqaYVvGqwUZptZa71pkYSPIS6gHTt2YN26dQCAsWPHYsmSJUaIYWu0SHvNGpquqFR1OG3Rj/w9vqXBrZK6zHIW5kT3FYDL5cKKFSvw3nvvYceOHfjyyy9RXl6utxi2R8msrLSyFpnFe1U5aH7a7T0jmgFyo15HOLMmkaL7CqCtrQ1utxsulwudOnVCa2srEhLs+xAbSbBzaMPJ3dYqn58b9cThzJpEgu4GIDk5Gc8++yyysrKQlJSEu+++G3/zN3+jtxgkCJFkCWmhlLhRjxD10X0j2LfffouCggKsX78eKSkp+OlPf4rhw4djzpw5eopBgvDDgp2Sm7dOFmfrLQ4hRAN0XwHs27cPGRkZ6N69OwBg6tSp2LRpk2IDwJ3A+iCXu22HcbDb7+2LXfserf2W2wmsexD41ltvRXl5Oa5fvw5BELB3717cdttteotBgsDcbUKiH91XAPfeey+++eYbTJ06FXFxcbjtttswd+5cvcUwFCvsaPXI8075XzocCk8IiQ5YDE4hai0P1SwMpgfRuiwOhl37Ddi379Hab1O5gOwOd7QSQswCi8HpjJ12tFrB1UWInaEB0Bm7VEbU6hAQGhVC1IMuIJ2xS3aNFq4uVgQlRF1oAHTGLvVbtHB1MX5CiLrQBWQAdqjfooWry07xE0L0gCsAoglauLpYEZQQdaEBIJqghavLLvETQvSCLiCiGWq4ugKzfrKH9cD+P11mFhAhKkADQEyLWCrpzmPnozJoTogR0AVETAuzfgjRFhoAYlqY9UOIttAAENPCrB9CtIUGgJgWZv0Qoi0MAhPTwnOACdEWGgBiauywa5oQo6ALiBBCbAoNACGE2BQaAEIIsSk0AIQQYlNoAAghxKbQABBCiE2hASCEEJui+z6ArVu34j/+4z+8r2tqajB58mQsX75cb1EIIcTW6G4AHn74YTz88MMAgBMnTmD+/PlYsGCB3mIQQojtMdQF9MILL+C5557DTTfdZKQYhBBiSwwrBVFeXo7GxkZkZWWF9L3u3ZM1kig4aWkphrVtJOy3/bBr3+3Wb8MMwObNmzF79uyQv3fxYgPcbkEDieRJS0vBhQv1urdrNOy3/bBr36O1306nQ3LibIgLqLm5GYcPH8a4ceOMaJ4QQggMMgDHjx9H//790alTJyOaJ4QQAoNcQKdOnULPnj2NaNpSlFbWshY+IUQzDDEAkyZNwqRJk4xo2jKUVtZi5e4T3kPRz9U3YeXuEwBAI0AIUQXuBDYpb39R7VX+Hhpb3Xj7i2pjBCKERB00ACaltr4ppPcJISRUaABMSnpKQkjvE0JIqNAAmJR5Y/ojMdb/50mMdWLemP7GCEQIiTp4KLxJ8QR6mQVECNEKGgATkzU0nQqfEKIZdAERQohNoQEghBCbQgNACCE2hTEAYllYKoOQyKABIJaEpTIIiRy6gIglYakMQiKHBoBYEpbKICRyaACIJWGpDEIihwaAWBKWyiAkchgEJpaEpTIIiRwaAGJZWCqDkMigC4gQQmwKDQAhhNgUGgBCCLEpNACEEGJTLBcEdjodtmzbSNhv+2HXvkdjv+X65BAEQdBRFkIIISaBLiBCCLEpNACEEGJTaAAIIcSm0AAQQohNoQEghBCbQgNACCE2hQaAEEJsCg0AIYTYFBoAQgixKTQACjly5AimT5+OyZMn47HHHsPp06eNFkl3fv7zn+PNN980WgzNKSkpwaRJkzBhwgRs3LjRaHF0paGhATk5OaipqTFaFN1Ys2YNsrOzkZ2djVdeecVocXSFBkAhixYtwksvvYQdO3YgNzcXL730ktEi6UZ9fT2WLl2KDRs2GC2K5tTW1uL111/Hpk2bsH37dnzwwQeoqqoyWixdqKiowMyZM1FdXW20KLpRXl6Offv24aOPPsL27dtx7Ngx7Nmzx2ixdIMGQAHNzc149tlnceuttwIAhgwZgrNnzxoslX6UlZWhf//+mD17ttGiaE55eTlGjRqFrl27olOnTnjwwQfxu9/9zmixdGHLli0oKipCjx49jBZFN9LS0lBQUID4+HjExcVh4MCBOHPmjNFi6YblqoEaQXx8PCZPngwAcLvdWLNmDcaPH2+wVPoxZcoUALCF++f8+fNIS0vzvu7RoweOHj1qoET6sWLFCqNF0J1bbrnF+//q6mqUlpbi/fffN1AifaEBCKC0tBSrVq3ye2/AgAH49a9/jebmZhQUFKC1tRX5+fkGSagdcn23C263Gw7H9+VzBUHwe02ikxMnTiA/Px+LFy9G//79jRZHN2gAAsjKykJWVlaH969du4annnoKXbt2xS9+8QvExcUZIJ22SPXdTvTs2RNffvml9/WFCxds5RKxI0eOHMEzzzyDpUuXIjs722hxdIUxAIUsWrQIN998M37+858jPj7eaHGIRowePRoHDhzApUuX4HK5sHv3bowdO9ZosYhGnD17FvPnz8fq1attp/wBrgAU8c0336CsrAyDBg3C3//93wNo9w2/++67BktG1CY9PR3PPfccZs2ahZaWFkyfPh3Dhw83WiyiEevXr0dTUxOKi4u9782YMQMzZ840UCr94IlghBBiU+gCIoQQm0IDQAghNoUGgBBCbAoNACGE2BQaAEIIsSk0AISEwbFjxzBixAh8/fXX3vcuXbqE8ePH47PPPjNOMEJCgGmghITJ5s2bsW7dOnz00Ufo3LkzHn/8cWRkZOCpp54yWjRCFEEDQEgELFq0CNevX0e/fv1w6tQpvPnmm6wdRCwDDQAhEXD9+nVMmTIFra2tKCkpQefOnY0WiRDFMAZASAScPHkS165dQ11dHY4dO2a0OISEBFcAhITJpUuXMH36dPzkJz9BU1MTXn/9dXz00Ud+5wkQYmZoAAgJg7a2Njz++OMYNGgQli1bBgAoLCzEqVOn8O///u+IiYkxWEJCgkMXECFh8Morr8DlcmHJkiXe95YvX46rV6/iZz/7mYGSEaIcrgAIIcSmcAVACCE2hQaAEEJsCg0AIYTYFBoAQgixKTQAhBBiU2gACCHEptAAEEKITaEBIIQQm/L/Ac0XuymllCMqAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Generate data\n",
    "x = np.random.randn(200)\n",
    "y = 10+0.5*x+np.random.randn(len(x))\n",
    "\n",
    "# Scatter plot\n",
    "plt.scatter(x,y)\n",
    "# This one is quite similar to \"plot(x,y,'.')\"\n",
    "plt.title('Scatter plot of data')\n",
    "plt.xlabel('X')\n",
    "plt.ylabel('Y')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## LineFit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "M = np.vstack((np.ones(len(x)), x)).T\n",
    "pars = np.linalg.lstsq(M,y, rcond=None)[0]\n",
    "intercept = pars[0]\n",
    "slope = pars[1]\n",
    "plt.scatter(x,y)\n",
    "plt.plot(x, intercept + slope*x, 'r')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "celltoolbar": "Slideshow",
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
